most citedCustomizing General-Purpose Foundation Models for Medical Report Generation

4 citations · 8 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG2024

On the Benefits of Over-parameterization for Out-of-Distribution Generalization

Yifan Hao, Yong Lin, Difan Zou +1

In recent years, machine learning models have achieved success based on the independently and identically distributed assumption. However, this assumption can be easily violated in…

cs.CV2024

Language-Driven Visual Consensus for Zero-Shot Semantic Segmentation

Zicheng Zhang, Tong Zhang, Yi Zhu +4

The pre-trained vision-language model, exemplified by CLIP, advances zero-shot semantic segmentation by aligning visual features with class embeddings through a transformer decoder…

cs.CV20231 cited

PerceptionGPT: Effectively Fusing Visual Perception into LLM

Renjie Pi, Lewei Yao, Jiahui Gao +2

The integration of visual inputs with large language models (LLMs) has led to remarkable advancements in multi-modal capabilities, giving rise to visual large language models (VLLM…

cs.CV20232 cited

3D-Aware Hypothesis & Verification for Generalizable Relative Object Pose Estimation

Chen Zhao, Tong Zhang, Mathieu Salzmann

Prior methods that tackle the problem of generalizable object pose estimation highly rely on having dense views of the unseen object. By contrast, we address the scenario where onl…

cs.CE20231 cited

A deep transfer learning network for structural condition identification with limited real-world training data

Nengxin Bao, Tong Zhang, Ruizhi Huang +3

Structural condition identification based on monitoring data is important for automatic civil infrastructure asset management. Nevertheless, the monitoring data is almost always in…

cs.CV20234 cited

Customizing General-Purpose Foundation Models for Medical Report Generation

Bang Yang, Asif Raza, Yuexian Zou +1

Medical caption prediction which can be regarded as a task of medical report generation (MRG), requires the automatic generation of coherent and accurate captions for the given med…